1. Field of the Invention
This present invention is directed to a system which prioritizes repair of equipment in a complex integrated plant such as a nuclear or fossil fuel power plant and, more particularly, the present invention uses a common scale to determine the priority of repair of all equipment in the plant, including sensors and backup equipment, taking into account the confidence level in the malfunction being diagnosed, the potential consequential damage caused by the malfunction and the severity or rate at which damage is occurring.
2. Description of the Related Art
Conventional diagnostic software, as illustrated in FIG. 1, determines what piece of equipment in a plant is malfunctioning with a malfunction defined as when an object or process is not functioning as needed or desired. Artificial intelligence systems, as depicted in the block diagram of FIG. 1, take sensor data 10 and determine 12 whether the sensor data is valid by comparing the sensor data to thresholds, other limits and internal consistency relationships. Once the validity of the sensor data is determined, the sensor data is interpreted 14 with respect to the physical meaning of the sensor data within the context of the plant being monitored. The data, validity and interpretations are combined to yield validated interpretations which define a plant state. Next the system diagnoses 16 the malfunctions and determines the confidence level in these malfunctions from the plant state. Current practice is to order the list of malfunctions based on confidence level. This conventional diagnostic system depicted in FIG. 1 is described in U.S. Pat. No. 4,644,479 incorporated by reference herein.
In actual practice the sorting based on confidence level usually places the malfunctioning sensors at the top of the list. Since the sensors are not worth shutting down the plant to repair, the plant operators generally ignore the malfunctions with the highest confidence level. As a result the malfunctions with the highest confidence have the lowest priority to an operator. Because of this problem, the operator scans down the list until he finds a malfunction which is significant to the continued operation of the plant. The problem is complicated because the most important malfunctions may be very far down on the list and may be missed. The operator is generally trying to determine how bad the malfunction is hurting the plant. This determination is made subjectively based on plant history and the experience of the operator. The operator makes, after reviewing the malfunction, a decision as to whether the malfunction and the associated equipment should be repaired. Because this approach to scheduling equipment repair is highly subjective a need has arisen for a system that automatically determines relative value of competing equipment repair options and takes into consideration the many factors normally considered by the operator such as the cost of an outage to fix the malfunctioning equipment verses the damage caused by allowing the malfunction to continue.